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Öğe 1D-local binary pattern based feature extraction for classification of epileptic EEG signals(Elsevier Science Inc, 2014) Kaya, Yilmaz; Uyar, Murat; Tekin, Ramazan; Yildirim, SelcukIn this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented. For the importance of making the right decision, the proposed method was performed to be able to get better features of the EEG signals. The proposed method was consisted of two stages: feature extraction by 1D-LBP and classification by classifier algorithms with features extracted. On the classification stage, the several machine learning methods were employed to uniform and non-uniform 1D-LBP features. The proposed method was also compared with other existing techniques in the literature to find out benchmark for an epileptic data set. The implementation results showed that the proposed technique could acquire high accuracy in classification of epileptic EEG signals. Also, the present paper is an attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals. (C) 2014 Elsevier Inc. All rights reserved.Öğe 1D-local binary pattern based feature extraction forclassification of epileptic EEG signals(2014) Kaya, Yılmaz; Uyar, Murat; Tekin, Ramazan; Yıldırım, SelçukIn this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented. For the importance of making the right decision, the proposed method was performed to be able to get better features of the EEG signals. The proposed method was consisted of two stages: feature extraction by 1D-LBP and classification by classifier algorithms with features extracted. On the classification stage, the several machine learning methods were employed to uniform and non-uniform 1D-LBP features. The proposed method was also compared with other existing techniques in the literature to find out benchmark for an epileptic data set. The implementation results showed that the proposed technique could acquire high accuracy in classification of epileptic EEG signals. Also, the present paper is an attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals.Öğe A PATTERN RECOGNITION APPROACH FOR CLASSIFICATION OF POWER QUALITY DISTURBANCE TYPES(Gazi Univ, Fac Engineering Architecture, 2011) Uyar, Murat; Yildirim, Selcuk; Gencoglu, Muhsin TunayIn this study, an algorithm based on pattern recognition approach is proposed for classification of power quality disturbance types. For feature extraction which is an important part of the pattern recognition, a method based on entropy which uses the decomposition coefficients of wavelet transform is presented. The most important advantage of the method is the reduction of data size without losing main distinguishing characteristics of signal. Support vector machines based on statistical learning theory is used as a classifier. The performance of the proposed algorithm is evaluated by using real and synthetic power quality disturbance data. Real power quality disturbance data are obtained from our national power system. Besides, the synthetic power quality disturbance data are obtained from ATP/EMTP and mathematical models. The analyses and results obtained in this study show that proposed algorithm has an efficient, feasible and practical structure.Öğe A pattern recognition approach for classification of power quality disturbance types(2011) Uyar, Murat; Yildirim, Selçuk; Genço?lu, Muhsin TunayIn this study, an algorithm based on pattern recognition approach is proposed for classification of power quality disturbance types. For feature extraction which is an important part of the pattern recognition, a method based on entropy which uses the decomposition coefficients of wavelet transform is presented. The most important advantage of the method is the reduction of data size without losing main distinguishing characteristics of signal. Support vector machines based on statistical learning theory is used as a classifier. The performance of the proposed algorithm is evaluated by using real and synthetic power quality disturbance data. Real power quality disturbance data are obtained from our national power system. Besides, the synthetic power quality disturbance data are obtained from ATP/EMTP and mathematical models. The analyses and results obtained in this study show that proposed algorithm has an efficient, feasible and practical structure.Öğe An Efficient Rotation Invariant Feature Extraction Method Based on Ring Projection Technique(IEEE, 2013) Atas, Musa; Kaya, Yilmaz; Uyar, MuratThis study presents an efficient rotation-invariant feature extraction method based on ring projection technique. The main advantage of this method is to reduce the number of sampling frequency of standard ring projection method. The proposed method is compared with the ring projection and local binary patterns according to the computational speed of the feature extraction and classification accuracy. By incrementally rotating first image of each texture class by 30 and 45 degrees enrich the dataset and yield two texture datasets having totally 1332 and 888 samples from the original Brodatz texture image dataset, respectively. Throughout the study Weka machine learning and data mining tool is utilized. As a classifier Naive Bayes, Bagging and J48 decision tree are used due to their simplicity and speed. Classification performance is evaluated via 10 fold cross validation technique. It is observed that, the proposed method outperforms other alternatives in terms of classification accuracy and feature extraction speed.Öğe An efficient rotation invariant feature extraction method based on ring projection technique(2013) Atas, Musa; Kaya, Yilmaz; Uyar, MuratThis study presents an efficient rotation-invariant feature extraction method based on ring projection technique. The main advantage of this method is to reduce the number of sampling frequency of standard ring projection method. The proposed method is compared with the ring projection and local binary patterns according to the computational speed of the feature extraction and classification accuracy. By incrementally rotating first image of each texture class by 30 and 45 degrees enrich the dataset and yield two texture datasets having totally 1332 and 888 samples from the original Brodatz texture image dataset, respectively. Throughout the study Weka machine learning and data mining tool is utilized. As a classifier Naive Bayes, Bagging and J48 decision tree are used due to their simplicity and speed. Classification performance is evaluated via 10 fold cross validation technique. It is observed that, the proposed method outperforms other alternatives in terms of classification accuracy and feature extraction speed. © 2013 IEEE.Öğe Analyzing of thermal mixing phenomena in a rectangular channel with twin jets by using artificial neural network(Elsevier Science Sa, 2013) Kok, Besir; Uyar, Murat; Varol, Yasin; Koca, Ahmet; Oztop, Hakan F.In this study, an experimental investigation was carried out to analyze the thermal mixing phenomena in a rectangular cross-section narrow channel. The channel has two circular water jet inlets, in different temperatures and a circular exit hole to supply continuity of mass. Several parameters were used in the experiments to control thermal mixing efficiency such as inclination angle of the channel, ratio of flow rate of inlet fluid, temperature difference between hot and cold jets and jet inlets diameters. Thermal mixing index was calculated from the measured temperatures. In order to reduce experimental time, an artificial neural network (ANN) model was built with limited number of measurements for a forward model. The obtained results indicated that estimation data of ANN shows that the ANN model can predict the output parameters without carry out any experiment. (C) 2013 Elsevier B.V. All rights reserved.Öğe Application of extreme learning machine for estimating solar radiation from satellite data(Wiley-Blackwell, 2014) Sahin, Mehmet; Kaya, Yilmaz; Uyar, Murat; Yildirim, SelcukIn this paper, a simple and fast method based on extreme learning machine (ELM) for the estimation of solar radiation in Turkey was presented. To design the ELM model, satellite data of the National Oceanic and Atmospheric Administration advanced very high-resolution radiometer from 20 locations spread over Turkey were used. The satellite-based land surface temperature, altitude, latitude, longitude, month, and city were applied as input to the ELM, and the output variable is the solar radiation. To show the applicability of the ELM model, a performance comparison in terms of the estimation capability and the learning speed was made between the ELM model and conventional artificial neural network (ANN) model with backpropagation. The comparison results showed that the ELM model gave better estimation than the ANN model for the overall test locations. Moreover, the ELM model was about 23.5 times faster than the ANN model. The method could be used by researchers or scientists to design high-efficiency solar devices such as solar power plant and photovoltaic cell. Copyright (c) 2013 John Wiley & Sons, Ltd.Öğe Application of extreme learning machine for estimating solar radiation from satellite data. Internatıonal Journal Of Energy Research, (2013) 38(2), 205-212(2013) Şahin, Mehmet; Kaya, Yılmaz; Uyar, Murat; Yıldırım, SelçukIn this paper, a simple and fast method based on extreme learning machine (ELM) for the estimation of solar radiation in Turkey was presented. To design the ELM model, satellite data of the National Oceanic and Atmospheric Administration advanced very high-resolution radiometer from 20 locations spread over Turkey were used. The satellite-based land surface temperature, altitude, latitude, longitude, month, and city were applied as input to the ELM, and the output variable is the solar radiation. To show the applicability of the ELM model, a performance comparison in terms of the estimation capability and the learning speed was made between the ELM model and conventional artificial neural network (ANN) model with backpropagation. The comparison results showed that the ELM model gave better estimation than the ANN model for the overall test locations. Moreover, the ELM model was about 23.5 times faster than the ANN model. The method could be used by researchers or scientists to design high-efficiency solar devices such as solar power plant and photovoltaic cell.Öğe Automatic identification of butterfly species based on local binary patterns and artificial neural network(Elsevier, 2015) Kaya, Yilmaz; Kayci, Lokman; Uyar, MuratButterflies are classified firstly according to their outer morphological qualities. It is required to analyze genital characters of them when classification according to outer morphological qualities is not possible. Genital characteristics of a butterfly can be determined by using various chemical substances and methods. Currently, these processes are carried out manually by preparing genital slides of the collected butterfly through some certain processes. For some groups of butterflies molecular techniques should be applied for identification which is expensive to use. In this study, a computer vision method is proposed for automatically identifying butterfly species as an alternative to conventional identification methods. The method is based on local binary pattern (LBP) and artificial neural network (ANN). A total of 50 butterfly images of five species were used for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has achieved well recognition in terms of accuracy rates for butterfly species identification. (C) 2014 Elsevier B.V. All rights reserved.Öğe Automatic identification of butterfly species based on local binary patterns and artificial neural network."Applied Soft Computing 28 (2015): 132-137.(2015) Kaya, Yılmaz; Kaycı, Lokman; Uyar, MuratButterflies are classified firstly according to their outer morphological qualities. It is required to analyze genital characters of them when classification according to outer morphological qualities is not possible. Genital characteristics of a butterfly can be determined by using various chemical substances and methods. Currently, these processes are carried out manually by preparing genital slides of the collected butterfly through some certain processes. For some groups of butterflies molecular techniques should be applied for identification which is expensive to use. In this study, a computer vision method is proposed for automatically identifying butterfly species as an alternative to conventional identification methods. The method is based on local binary pattern (LBP) and artificial neural network (ANN). A total of 50 butterfly images of five species were used for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has achieved well recognition in terms of accuracy rates for butterfly species identification.Öğe Classification of Power Quality Disturbances Based on S-Transform and Image Processing Techniques(IEEE, 2013) Uyar, Murat; Kaya, Yilmaz; Atas, MusaThis paper presents a method that combines discrete S-transform (DST) time-frequency distribution (TFD) and local binary pattern (LBP) based image analysis technique for classifying power quality (PQ) disturbances. The purpose of this combination is to extract discriminative features by utilizing from both capability of generating the compact TFD of a non-stationary signal and the efficient image representation capability of LBP. In the proposed method, DST based TFDs of PQ disturbance signals are considered as 2-D images. LBP histograms are used to extract the features from TF images. Initially, the uniform patterns in TF images are obtained by the LBP operator. Next, the occurrence histograms of these patterns are used to produce representative feature vectors that can capture the unique and salient characteristics of each disturbance. The classification performance of the proposed method is evaluated through total 2400 disturbance signals. The experimental results have shown that the rate of correct classification is about 98 % for the different PQ disturbances.Öğe Classification of power quality disturbances based on s-transform and image processing techniques(2013) Uyar, Murat; Kaya, Yilmaz; Atas, MusaThis paper presents a method that combines discrete S-transform (DST) time-frequency distribution (TFD) and local binary pattern (LBP) based image analysis technique for classifying power quality (PQ) disturbances. The purpose of this combination is to extract discriminative features by utilizing from both capability of generating the compact TFD of a nonstationary signal and the efficient image representation capability of LBP. In the proposed method, DST based TFDs of PQ disturbance signals are considered as 2-D images. LBP histograms are used to extract the features from TF images. Initially, the uniform patterns in TF images are obtained by the LBP operator. Next, the occurrence histograms of these patterns are used to produce representative feature vectors that can capture the unique and salient characteristics of each disturbance. The classification performance of the proposed method is evaluated through total 2400 disturbance signals. The experimental results have shown that the rate of correct classification is about 98 % for the different PQ disturbances. © 2013 IEEE.Öğe Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data(Elsevier Sci Ltd, 2013) Sahin, Mehmet; Kaya, Yilmaz; Uyar, MuratIn this paper, the estimation capacities of MLR and ANN are investigated to estimate monthly-average daily SR over Turkey. The satellite data are used for 73 different locations over Turkey. Land surface temperature, altitude, latitude, longitude and month are offered as the input variables for modeling ANN and MLR to get SR. Estimations of SR are evaluated with the meteorological values by using the statistical bases. The obtained results indicated that the ANN model could achieve a satisfactory performance when compared to the MLR model. Moreover, it is understood that more accurate results in estimation of SR are obtained in the use of satellite data, rather than the use of meteorological station data. Finally, the built ANN model is used to estimate the yearly average of daily SR over Turkey. As a result, satellite-based SR map for Turkey is generated. (C) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.Öğe Comparison of ANN and MLR models for estimating solar radiation in Turkeyusing NOAA/AVHRR data. Advances in Space Research 51 (2013) 891- 904(2013) Şahin, Mehmet; Kaya, Yılmaz; Uyar, MuratIn this paper, the estimation capacities of MLR and ANN are investigated to estimate monthly-average daily SR over Turkey. The satellite data are used for 73 different locations over Turkey. Land surface temperature, altitude, latitude, longitude and month are offered as the input variables for modeling ANN and MLR to get SR. Estimations of SR are evaluated with the meteorological values by using the statistical bases. The obtained results indicated that the ANN model could achieve a satisfactory performance when compared to the MLR model. Moreover, it is understood that more accurate results in estimation of SR are obtained in the use of satellite data, rather than the use of meteorological station data. Finally, the built ANN model is used to estimate the yearly average of daily SR over Turkey. As a result, satellite-based SR map for Turkey is generated.Öğe Güç Kalitesi Problemlerinin Analizi İçin İşaret İşleme Yöntemlerinin Karşılaştırılması(2013) Uyar, Murat; Yıldırım, Selçuk; Gençoğlu, Muhsin TunayBu makalede güç kalitesi problemlerinin analizinde kullanılan üç farklı işaret işleme yönteminin performansıkarşılaştırmalı bir şekilde incelenmiştir. İncelenen yöntemler, kısa zamanlı Fourier dönüşümü (KZFD), dalgacıkdönüşümü (DD) ve S dönüşümü (SD)dür. Karşılaştırmalar, yöntemlerin güç kalitesi bozulma işaretlerininanalizinde göstermiş oldukları başarım ve hesaplama süresi bakımından gerçekleştirilmiştir. Karşılaştırma içinyapılan analizlerde, gerilim çökmesi, harmonik ve salınımlı geçici durum gibi, sadece zaman, sadece frekans vehem zaman hem de frekans bilgisi içeren üç farklı güç kalitesi bozulma işareti kullanılmıştır. Analizlerden vekarşılaştırma sonuçlarından SDnin güç kalitesi bozulmalarının analizi için ayırt edici karakteristiklere sahipolduğu görülmüştür. Bu yaklaşım zamana göre değişim gösteren örüntü verilerinden bilgi çıkarımı,benzerliklerin belirlenmesi ve örüntü sınıflandırma problemlerinin çözümü için uygulanabilir.Öğe A Hybrid Decision Support System Based on Rough Set and Extreme Learning Machine for Diagnosis of Hepatitis Disease(2014) Kaya, Yılmaz; Uyar, MuratHepatitis is a disease which is seen at all levels of age. Hepatitis disease solely does not have a lethal effect, but the early diagnosis and treatment of hepatitis is crucial as it triggers other diseases. In this study, a new hybrid medical decision support system based on rough set (RS) and extreme learning machine (ELM) has been proposed for the diagnosis of hepatitis disease. RS-ELM consists of two stages. In the first one, redundant features have been removed from the data set through RS approach. In the second one, classification process has been implemented through ELM by using remaining features. Hepatitis data set, taken from UCI machine learning repository has been used to test the proposed hybrid model. A major part of the data set (48.3%) includes missing values. As removal of missing values from the data set leads to data loss, feature selection has been done in the first stage without deleting missing values. In the second stage, the classification process has been performed through ELM after the removal of missing values from sub-featured data sets that were reduced in different dimensions. The results showed that the highest 100.00% classification accuracy has been achieved through RS-ELM and it has been observed that RS-ELM model has been considerably successful compared to the other methods in the literature. Furthermore in this study, the most significant features have been determined for the diagnosis of the hepatitis. It is considered that proposed method is to be useful in similar medical applications.Öğe ST and LSSVR-based the fault location algorithm for the series compensated power transmission lines(Sila Science, 2012) Uyar, MuratThis paper presents an approach based on S-Transform (ST) and Least Square Support Vector Regression (LSSVR) techniques for predicting the fault location in a compensated power transmission line with the fixed series capacitor. The entropy features of ST matrix are extracted for reducing the dimension of three-phase current signal measured from the sending end of the transmission line. Then, the extracted features are applied as input to LSSVR for determining fault location on series compensated line (SCL). The presented method has been tested using model of a 400kV, 320km transmission line, which is compensated, by a three-phase capacitor bank in the middle. The results show that the proposed method is capable of determining fault location on SCL under wide variations in operating conditions (i.e. fault resistance, fault inception angle, fault distance, percentage compensation level, source impedance and load angle).
